Application-level observability for adaptive Edge to Cloud continuum systems
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912837516918784 |
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| author | Sidi, Kaddour Balouek, Daniel Jonglez, Baptiste |
| author_facet | Sidi, Kaddour Balouek, Daniel Jonglez, Baptiste |
| contents | Modern Edge-to-Cloud (E2C) systems require fine-grained observability to ensure adaptive behavior and compliance with performance objectives across heterogeneous and dynamic environments. This work introduces an application-level observability framework that integrates developer-driven instrumentation and SLO-aware feedback for autonomous adaptation. By combining OpenTelemetry, Prometheus, K3s, and Chaos Mesh, the framework enables real-time monitoring and adaptive control across the continuum. A video processing use case demonstrates how application-level metrics guide automatic adjustments to maintain target frame rate, latency, and detection accuracy under variable workloads and injected faults. Preliminary results highlight improved scalability, fault tolerance, and responsiveness, providing a practical foundation for adaptive, SLO-compliant E2C applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14923 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Application-level observability for adaptive Edge to Cloud continuum systems Sidi, Kaddour Balouek, Daniel Jonglez, Baptiste Distributed, Parallel, and Cluster Computing Modern Edge-to-Cloud (E2C) systems require fine-grained observability to ensure adaptive behavior and compliance with performance objectives across heterogeneous and dynamic environments. This work introduces an application-level observability framework that integrates developer-driven instrumentation and SLO-aware feedback for autonomous adaptation. By combining OpenTelemetry, Prometheus, K3s, and Chaos Mesh, the framework enables real-time monitoring and adaptive control across the continuum. A video processing use case demonstrates how application-level metrics guide automatic adjustments to maintain target frame rate, latency, and detection accuracy under variable workloads and injected faults. Preliminary results highlight improved scalability, fault tolerance, and responsiveness, providing a practical foundation for adaptive, SLO-compliant E2C applications. |
| title | Application-level observability for adaptive Edge to Cloud continuum systems |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.14923 |